A Comparative Study on Handwritten Digits Recognition Using Classifiers SVM, Multi Class Perception and KNN

Project Code :TCMAPY893

Objective

The objective of A Comparative Study on Handwritten Digits Recognition Using Classifiers SVM, Multi Class Perception, and KNN is to compare the performance of three different classification algorithms on recognizing handwritten digits. The study uses support vector machines (SVM), multi-class perceptron, and K-nearest neighbors (KNN) algorithms to classify the digits, based on features such as pixel values and their coordinates.

Abstract

In this project we present an innovative method for offline handwritten character detection using deep neural networks. In today world it has become easier to train deep neural networks because of availability of huge amount of data and various Algorithmic innovations which are taking place. Now-a-days the amount of computational power needed to train a neural network has increased due to the availability of GPU’s and other cloud-based services like Google Cloud platform and Amazon Web Services which provide resources to train a Neural network on the cloud. We have designed a image segmentation based Handwritten character recognition system. In our system we have made use of OpenCV for performing Image processing and have used TensorFlow for training a neural Network. We have developed this system using python programming language.

KEYWORDS: Handwritten, Knn, Cnn, Svm, Character, segmentation.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.